Research on global path planning method based on improved ant colony algorithm

In mobile robot navigation, the design of path planning algorithms is a key issue, because the fast and efficient work of mobile robots must rely on reasonable and reliable path planning algorithms. The ant colony method is used to study the path planning of mobile robots in this work. The mathematical model of various ant colony method parameters has been updated and optimized. The direction guiding factor is introduced to avoid the blindness of the traditional ant colony algorithm. An adaptive pheromone updating rule was proposed to improve the disadvantages of the traditional ant colony algorithm, which is easy to fall into premature and outputs local optimum. The adaptive adjustment pheromone volatile factor is adaptively to make the algorithm level more distinct, highlight the task focus of each stage, and consider the quality and efficiency of the path planning. Finally, complex and open environment maps are designed, and simulation experiments are carried out to verify the advance of the improved algorithm in the path planning distance and convergence speed.